An alternative method is to use dilated convolution, instead of downscaling, to enlarge receptive fields. In [1]}, [2]}, multiple convolutional layers with different dilation rates are used to extract feature maps at different scales. These feature maps have the same spatial sizes and are well aligned spatially. They are concatenated and fused by \(1\times 1\)  convolution to exploit multi-scale information. However, these dilation rates are still manually set and fixed, which may restrict the generalization ability over various human sizes.
